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action_timeline

Reconstruct a chronological action ledger per session or principal, showing memory digests and oversight events to meet agentic AI traceability requirements.

Instructions

One workflow reconstructed from the action ledger: the entries in order, content-free, filtered to a session or a principal when given, each with the memory digest the agent held, the model, the actor and the oversight or incident events that refer to it. The traceability the CNIL's 2026 note on agentic AI asks for. Nothing is inferred; a row carries only what the entry recorded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sessionNo
principalNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.39.0

TDQS

B3.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It does well by disclosing content-free rows, optional session/principal filtering, ordering, and the strong guarantee that nothing is inferred and each row carries only recorded data. It does not explicitly state read-only status, but the reconstruction language implies it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single long, clause-heavy sentence followed by a short reinforcing sentence. It is moderately sized but dense and somewhat harder to parse quickly. The CNIL reference provides context but is not essential to invoking the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description lists the row fields, ordering, filtering, and non-inference guarantees, which is enough for an agent to understand the return shape. Minor gaps remain around pagination, result limits, and error behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has two parameters with 0% description coverage, so the description must compensate. It does explain that session and principal are the filters used when provided, which adds meaning. However, it does not clarify accepted value formats, whether the filters combine, or exact filtering behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as reconstructing a workflow from the action ledger, with ordered, content-free entries and per-row metadata like memory digest, model, actor, and related events. It states the resource and output shape, though it does not name a sibling to differentiate it from.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus the many audit, compliance, or history siblings. The CNIL-traceability phrase hints at a compliance use case, but it does not provide selection criteria, exclusions, or alternative tool names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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